Machine Learning

Discriminative and Generative

Tony Jebara (Autor)

Buch | Softcover

200 Seiten

2012 | Softcover reprint of the original 1st ed. 2004
Springer-Verlag New York Inc.
978-1-4613-4756-9 (ISBN)

Artikel merken

Machine Learning: Discriminative and Generative covers the main contemporary themes and tools in machine learning ranging from Bayesian probabilistic models to discriminative support-vector machines. However, unlike previous books that only discuss these rather different approaches in isolation, it bridges the two schools of thought together within a common framework, elegantly connecting their various theories and making one common big-picture. Also, this bridge brings forth new hybrid discriminative-generative tools that combine the strengths of both camps. This book serves multiple purposes as well. The framework acts as a scientific breakthrough, fusing the areas of generative and discriminative learning and will be of interest to many researchers. However, as a conceptual breakthrough, this common framework unifies many previously unrelated tools and techniques and makes them understandable to a larger portion of the public. This gives the more practical-minded engineer, student and the industrial public an easy-access and more sensible road map into the world of machine learning.

Machine Learning: Discriminative and Generative is designed for an audience composed of researchers & practitioners in industry and academia. The book is also suitable as a secondary text for graduate-level students in computer science and engineering.

1. Introduction.- 2. Generative Versus Discriminative Learning.- 3. Maximum Entropy Discrimination.- 4. Extensions to Med.- 5. Latent Discrimination.- 6. Conclusion.- 7. Appendix.

Erscheint lt. Verlag	27.9.2012
Reihe/Serie	The Springer International Series in Engineering and Computer Science ; 755
Zusatzinfo	XVII, 200 p.
Verlagsort	New York, NY
Sprache	englisch
Maße	155 x 235 mm
Themenwelt	Mathematik / Informatik ► Informatik ► Datenbanken
	Informatik ► Theorie / Studium ► Künstliche Intelligenz / Robotik
	Mathematik / Informatik ► Mathematik
ISBN-10	1-4613-4756-4 / 1461347564
ISBN-13	978-1-4613-4756-9 / 9781461347569
Zustand	Neuware